Explain the model evaluation metrics.

Question

Accepted Answer

Model evaluation metrics are used to assess the performance of a predictive model by comparing predicted values with actual values.

Common model evaluation metrics include accuracy, precision, recall, F1 score, ROC-AUC, and mean squared error.
Accuracy measures the proportion of correct predictions out of the total predictions made by the model.
Precision measures the proportion of true positive predictions out of all positive predictions made by the model.
Recall measures the proportion of true positive predictions out of all actual positive instances.
F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics.
ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) is used for binary classification models to evaluate the trade-off between true positive rate and false positive rate.
Mean squared error is used for regression models to measure the average of the squares of the errors between predicted and actual values.

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